Hospital readmission rates are a publicly reported hospital quality measure; hospitals use transitional care interventions, like follow-up phone calls, to lower readmission rates. However, these interventions are often resource-intensive, and current methods for deciding who should receive them may not be effective. This study aims to analyze which patients actually benefit from these interventions using advanced machine learning with the goal of redesigning the resource allocation strategy to more effectively prevent readmissions, potentially influencing future policies at Michigan Medicine.